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Novel Antenna Design for Surface Wave Suppression in Microwave Breast Screening

2023· article· en· W4388450150 on OpenAlexaff
Milad Mokhtari, Milica Popović

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrowaveGround planeAntenna (radio)Surface waveMaterials scienceAcousticsRadiation patternOptoelectronicsElectronic engineeringComputer scienceOpticsPhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

One of the challenges of antenna design for microwave-based breast screening systems is to minimize the surface waves propagating at the interface between the substrate and the biological tissue. The surface waves become additionally pronounced inside the substrate with a confining ground plane, which is required for unidirectionality and blocks environmental radiation. This paper introduces a simplified human breast model and presents a quantitative analysis of the existing surface waves. Then, we propose an array of 16 cavity-backed patch antennas with parasitic elements, designed to operate in the 3.1-5.1 GHz range. Each antenna element is optimized to function next to the breast tissue. Full-wave simulations reveal that the proposed antenna array achieves higher unidirectionality and suppressed mutual coupling levels. In addition, the design offers improved bio-compatibility when compared with the previous designs, as it employs the SYLGARD™ 184 silicone elastomer PDMS substrate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.233
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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